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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../getting_started/quick_start.html">Quick Start</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/notebooks.html">Legacy notebooks</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../cli/torchtrtc.html">torchtrtc</a></li>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../contributors/system_overview.html">System Overview</a></li>
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  <h1>Source code for torch_tensorrt._Input</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">__future__</span><span class="w"> </span><span class="kn">import</span> <span class="n">annotations</span>

<span class="kn">from</span><span class="w"> </span><span class="nn">enum</span><span class="w"> </span><span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._enums</span><span class="w"> </span><span class="kn">import</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">memory_format</span>


<div class="viewcode-block" id="Input"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input">[docs]</a><span class="k">class</span><span class="w"> </span><span class="nc">Input</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Defines an input to a module in terms of expected shape, data type and tensor format.</span>

<span class="sd">    Attributes:</span>
<span class="sd">        shape_mode (torch_tensorrt.Input._ShapeMode): Is input statically or dynamically shaped</span>
<span class="sd">        shape (Tuple or Dict): Either a single Tuple or a dict of tuples defining the input shape.</span>
<span class="sd">            Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form</span>

<span class="sd">            .. code-block:: py</span>

<span class="sd">                {&quot;min_shape&quot;: Tuple, &quot;opt_shape&quot;: Tuple, &quot;max_shape&quot;: Tuple}</span>

<span class="sd">        dtype (torch_tensorrt.dtype): The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
<span class="sd">        format (torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">class</span><span class="w"> </span><span class="nc">_ShapeMode</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
        <span class="n">STATIC</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">DYNAMIC</span> <span class="o">=</span> <span class="mi">1</span>

    <span class="n">shape_mode</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_ShapeMode</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
        <span class="kc">None</span>  <span class="c1">#: Is input statically or dynamically shaped</span>
    <span class="p">)</span>
    <span class="n">shape</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]]</span> <span class="o">=</span> <span class="p">(</span>
        <span class="kc">None</span>  <span class="c1">#: Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form ``{ &quot;min_shape&quot;: Tuple, &quot;opt_shape&quot;: Tuple, &quot;max_shape&quot;: Tuple }``</span>
    <span class="p">)</span>
    <span class="n">dtype</span><span class="p">:</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">(</span>
        <span class="n">dtype</span><span class="o">.</span><span class="n">unknown</span>
    <span class="p">)</span>  <span class="c1">#: The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
    <span class="n">_explicit_set_dtype</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="nb">format</span><span class="p">:</span> <span class="n">memory_format</span> <span class="o">=</span> <span class="p">(</span>
        <span class="n">memory_format</span><span class="o">.</span><span class="n">linear</span>
    <span class="p">)</span>  <span class="c1">#: The expected format of the input tensor (default: torch_tensorrt.memory_format.linear)</span>

    <span class="n">DOMAIN_OFFSET</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span>
    <span class="n">low_tensor_domain_incl</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="n">high_tensor_domain_excl</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">low_tensor_domain_incl</span> <span class="o">+</span> <span class="n">DOMAIN_OFFSET</span>
    <span class="n">torch_tensor</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
    <span class="n">is_shape_tensor</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>

<div class="viewcode-block" id="Input.__init__"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;__init__ Method for torch_tensorrt.Input</span>

<span class="sd">        Input accepts one of a few construction patterns</span>

<span class="sd">        Args:</span>
<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>

<span class="sd">        Keyword Arguments:</span>
<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>
<span class="sd">            min_shape (Tuple or List, optional): Min size of input tensor&#39;s shape range</span>
<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implicitly this sets Input&#39;s shape_mode to DYNAMIC</span>
<span class="sd">            opt_shape (Tuple or List, optional): Opt size of input tensor&#39;s shape range</span>
<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implicitly this sets Input&#39;s shape_mode to DYNAMIC</span>
<span class="sd">            max_shape (Tuple or List, optional): Max size of input tensor&#39;s shape range</span>
<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implicitly this sets Input&#39;s shape_mode to DYNAMIC</span>
<span class="sd">            dtype (torch.dtype or torch_tensorrt.dtype): Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</span>
<span class="sd">            format (torch.memory_format or torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
<span class="sd">            tensor_domain (Tuple(float, float), optional): The domain of allowed values for the tensor, as interval notation: [tensor_domain[0], tensor_domain[1]).</span>
<span class="sd">                Note: Entering &quot;None&quot; (or not specifying) will set the bound to [0, 2)</span>
<span class="sd">            torch_tensor (torch.Tensor): Holds a corresponding torch tensor with this Input.</span>
<span class="sd">            name (str, optional): Name of this input in the input nn.Module&#39;s forward function. Used to specify dynamic shapes for the corresponding input in dynamo tracer.</span>
<span class="sd">        Examples:</span>
<span class="sd">            - Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</span>
<span class="sd">            - Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</span>
<span class="sd">            - Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># Compatibility code for switching over from InputTensorSpec</span>
        <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="s2">&quot;shape_ranges&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="k">assert</span> <span class="p">(</span>
                <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">3</span>
            <span class="p">)</span>
            <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]</span>

            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>

        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
                <span class="p">)</span>
            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Found that both shape (as a positional argument), and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
                <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>

        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span>
                <span class="nb">all</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">])</span>
            <span class="p">):</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Missing required arguments for class Input</span><span class="se">\n</span><span class="s2">Either shape or all three of min_shape, opt_shape, max_shape must be defined&quot;</span>
                <span class="p">)</span>
            <span class="k">elif</span> <span class="p">(</span><span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span>
                <span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
            <span class="p">):</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Found that both shape, and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
                <span class="p">)</span>

            <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]))</span>
                    <span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">])</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]))</span>
                        <span class="o">+</span> <span class="s2">&quot; for min_shape&quot;</span>
                    <span class="p">)</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]))</span>
                        <span class="o">+</span> <span class="s2">&quot; for opt_shape&quot;</span>
                    <span class="p">)</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]))</span>
                        <span class="o">+</span> <span class="s2">&quot; for max_shape&quot;</span>
                    <span class="p">)</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">{</span>
                    <span class="s2">&quot;min_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]),</span>
                    <span class="s2">&quot;opt_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]),</span>
                    <span class="s2">&quot;max_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]),</span>
                <span class="p">}</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;Unexpected number of positional arguments for class Input </span><span class="se">\n</span><span class="s2">    Found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span><span class="si">}</span><span class="s2"> arguments, expected either zero or a single positional arguments&quot;</span>
            <span class="p">)</span>

        <span class="k">if</span> <span class="s2">&quot;dtype&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">dtype</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">])</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">dtype</span><span class="o">.</span><span class="n">unknown</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">False</span>

        <span class="k">if</span> <span class="s2">&quot;is_shape_tensor&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">is_shape_tensor</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;is_shape_tensor&quot;</span><span class="p">]</span>

        <span class="k">if</span> <span class="s2">&quot;format&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">format</span> <span class="o">=</span> <span class="n">memory_format</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;format&quot;</span><span class="p">])</span>

        <span class="k">if</span> <span class="s2">&quot;tensor_domain&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="n">domain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;tensor_domain&quot;</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">domain</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_tensor_domain</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span>

        <span class="k">if</span> <span class="s2">&quot;torch_tensor&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">torch_tensor</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;torch_tensor&quot;</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_shape_tensor</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">torch_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
                    <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">]</span>
                <span class="p">)</span>
            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">torch_tensor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">example_tensor</span><span class="p">(</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">torch_tensor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">example_tensor</span><span class="p">()</span>

        <span class="k">if</span> <span class="s2">&quot;name&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span></div>

    <span class="k">def</span><span class="w"> </span><span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
            <span class="k">return</span> <span class="s2">&quot;Input(shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">, domain=[</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">))&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
            <span class="p">)</span>
        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
                <span class="k">return</span> <span class="s2">&quot;Input(min_shape=</span><span class="si">{}</span><span class="s2">, opt_shape=</span><span class="si">{}</span><span class="s2">, max_shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">, domain=[</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">))&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">],</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">],</span>
                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span>
                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">),</span>
                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
                <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Input shape is dynamic but shapes are not provided as dictionary (found: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
                <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unknown input shape mode&quot;</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__str__</span><span class="p">()</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">equivalent_spec</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">Input</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">a</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">!=</span> <span class="n">b</span><span class="o">.</span><span class="n">shape_mode</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">False</span>

        <span class="k">if</span> <span class="n">a</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span>
            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span>
            <span class="n">checks</span> <span class="o">=</span> <span class="p">[</span>
                <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">],</span>
                <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span>
                <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">],</span>
                <span class="n">a</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
                <span class="n">a</span><span class="o">.</span><span class="n">format</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">format</span><span class="p">,</span>
                <span class="n">a</span><span class="o">.</span><span class="n">low_tensor_domain_incl</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">low_tensor_domain_incl</span><span class="p">,</span>
                <span class="n">a</span><span class="o">.</span><span class="n">high_tensor_domain_excl</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">high_tensor_domain_excl</span><span class="p">,</span>
            <span class="p">]</span>
            <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="n">checks</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">checks</span> <span class="o">=</span> <span class="p">[</span>
                <span class="n">a</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
                <span class="n">a</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
                <span class="n">a</span><span class="o">.</span><span class="n">format</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">format</span><span class="p">,</span>
                <span class="n">a</span><span class="o">.</span><span class="n">low_tensor_domain_incl</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">low_tensor_domain_incl</span><span class="p">,</span>
                <span class="n">a</span><span class="o">.</span><span class="n">high_tensor_domain_excl</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">high_tensor_domain_excl</span><span class="p">,</span>
            <span class="p">]</span>
            <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="n">checks</span><span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">_supported_input_size_type</span><span class="p">(</span><span class="n">input_size</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
            <span class="k">return</span> <span class="kc">True</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
            <span class="k">return</span> <span class="kc">True</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
            <span class="k">return</span> <span class="kc">True</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">False</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">_parse_tensor_domain</span><span class="p">(</span>
        <span class="n">domain</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]],</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a tuple of integers which specifies a tensor domain in the interval format: [lo, hi)</span>

<span class="sd">        Args:</span>
<span class="sd">            domain (Tuple[int, int]): A tuple of integers (or NoneTypes) to verify</span>

<span class="sd">        Returns:</span>
<span class="sd">            A tuple of two int32_t-valid integers</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">domain</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">result_domain</span> <span class="o">=</span> <span class="p">(</span>
                <span class="n">Input</span><span class="o">.</span><span class="n">low_tensor_domain_incl</span><span class="p">,</span>
                <span class="n">Input</span><span class="o">.</span><span class="n">high_tensor_domain_excl</span><span class="p">,</span>
            <span class="p">)</span>

        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">domain_lo</span><span class="p">,</span> <span class="n">domain_hi</span> <span class="o">=</span> <span class="n">domain</span>

            <span class="c1"># Validate type and provided values for domain</span>
            <span class="n">valid_type_lo</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_lo</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span>
            <span class="n">valid_type_hi</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_hi</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span>

            <span class="k">if</span> <span class="ow">not</span> <span class="n">valid_type_lo</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Expected value for tensor domain low specifier, got </span><span class="si">{</span><span class="n">domain_lo</span><span class="si">}</span><span class="s2">&quot;</span>
                <span class="p">)</span>
            <span class="k">elif</span> <span class="ow">not</span> <span class="n">valid_type_hi</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Expected value for tensor domain high specifier, got </span><span class="si">{</span><span class="n">domain_hi</span><span class="si">}</span><span class="s2">&quot;</span>
                <span class="p">)</span>

            <span class="k">if</span> <span class="n">domain_hi</span> <span class="o">&lt;=</span> <span class="n">domain_lo</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Expected provided integer range to have low tensor domain value &quot;</span>
                    <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;&lt; high tensor domain value, got invalid range [</span><span class="si">{</span><span class="n">domain_lo</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">domain_hi</span><span class="si">}</span><span class="s2">)&quot;</span>
                <span class="p">)</span>
            <span class="n">result_domain</span> <span class="o">=</span> <span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">domain_lo</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="n">domain_hi</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;Expected 2 values for domain, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">domain</span><span class="si">}</span><span class="s2">&quot;</span>
            <span class="p">)</span>

        <span class="k">return</span> <span class="n">result_domain</span>

<div class="viewcode-block" id="Input.from_tensor"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensor">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">from_tensor</span><span class="p">(</span>
        <span class="bp">cls</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">disable_memory_format_check</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Input&quot;</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a Input which contains the information of the given PyTorch tensor.</span>

<span class="sd">        Args:</span>
<span class="sd">            tensor (torch.Tensor): A PyTorch tensor.</span>
<span class="sd">            disable_memory_format_check (bool): Whether to validate the memory formats of input tensors</span>

<span class="sd">        Returns:</span>
<span class="sd">            A Input object.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span>
            <span class="n">disable_memory_format_check</span>
            <span class="ow">or</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
            <span class="ow">or</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Tensor does not have a supported memory format, supported formats are contiguous or channel_last&quot;</span>
            <span class="p">)</span>
        <span class="n">frmt</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span>
            <span class="k">if</span> <span class="p">(</span>
                <span class="n">disable_memory_format_check</span>
                <span class="ow">or</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
            <span class="p">)</span>
            <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="n">frmt</span><span class="p">,</span> <span class="n">torch_tensor</span><span class="o">=</span><span class="n">t</span><span class="p">)</span></div>

<div class="viewcode-block" id="Input.from_tensors"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensors">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">from_tensors</span><span class="p">(</span>
        <span class="bp">cls</span><span class="p">,</span> <span class="n">ts</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">disable_memory_format_check</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;Input&quot;</span><span class="p">]:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a list of Inputs which contain</span>
<span class="sd">        the information of all the given PyTorch tensors.</span>

<span class="sd">        Args:</span>
<span class="sd">            tensors (Iterable[torch.Tensor]): A list of PyTorch tensors.</span>
<span class="sd">            disable_memory_format_check (bool): Whether to validate the memory formats of input tensors</span>

<span class="sd">        Returns:</span>
<span class="sd">            A list of Inputs.</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
        <span class="k">return</span> <span class="p">[</span>
            <span class="bp">cls</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">disable_memory_format_check</span><span class="o">=</span><span class="n">disable_memory_format_check</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">ts</span>
        <span class="p">]</span></div>

<div class="viewcode-block" id="Input.example_tensor"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.example_tensor">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">example_tensor</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span> <span class="n">optimization_profile_field</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Get an example tensor of the shape specified by the Input object</span>

<span class="sd">        Args:</span>
<span class="sd">            optimization_profile_field (Optional(str)): Name of the field to use for shape in the case the Input is dynamically shaped</span>

<span class="sd">        Returns:</span>
<span class="sd">            A PyTorch Tensor</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">optimization_profile_field</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Specified a optimization profile field but the input is static&quot;</span>
                <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
                    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
                        <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">use_default</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
                    <span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="ne">RuntimeError</span><span class="p">(</span>
                        <span class="sa">f</span><span class="s2">&quot;Input shape is dynamic but shapes are not provided as sequence (found: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
                    <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">optimization_profile_field</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="k">try</span><span class="p">:</span>
                    <span class="k">assert</span> <span class="nb">any</span><span class="p">(</span>
                        <span class="n">optimization_profile_field</span> <span class="o">==</span> <span class="n">field_name</span>
                        <span class="k">for</span> <span class="n">field_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
                    <span class="p">)</span>
                <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                        <span class="s2">&quot;Invalid field name, expected one of min_shape, opt_shape, max_shape&quot;</span>
                    <span class="p">)</span>

                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
                    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">optimization_profile_field</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
                        <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">use_default</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
                    <span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                        <span class="sa">f</span><span class="s2">&quot;Input shape is dynamic but shapes are not provided as dictionary (found: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
                    <span class="p">)</span>

            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Requested an example tensor from a dynamic shaped input but did not specific which profile field to use.&quot;</span>
                <span class="p">)</span>
        <span class="k">raise</span></div></div>
</pre></div>

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